Document Representations to Improve Topic Modelling

• Published in: 2020 International Conference on Software Security and Assurance (ICSSA) pp. 79 - 83
• Main Authors: Poojitha, P. Venkata, Menon, Remya R. K.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-10-2020
• Subjects: Approximation algorithms; clustering; Dictionaries; dictionary representation; document detection; Latent Dirichlet Allocation; Machine learning algorithms; Matching pursuit algorithms; Orthogonal Matching Pursuit; Semantics; sparse matrix; Sparse representation; TF-IDF; topic modeling
• DOI: 10.1109/ICSSA51305.2020.00022

Each and every day we are collecting lots of information from web applications. So it is difficult to understand or detect what the whole information is all about. To detect, understand and summarise the whole information we need some specific tools and techniques like topic modelling which helps to analyze and identify the crisp of the data. This paper implements the sparsity based document representation to improve Topic Modeling, it organizes the data with meaningful structure by using machine learning algorithms like LDA(Latent Dirichlet Allocation) and OMP(Orthogonal Matching Pursuit) algorithms. It identifies a documents belongs to which topic as well as similarity between documents in an existing dictionary. The OMP(Orthogonal Matching Pursuit) algorithm is the best algorithm for sparse approximation With better accuracy. OMP(Orthogonal Matching Pursuit) algorithm can identify the topics to which the input document[Y] is mostly related to across a large collection of text documents present in a dictionary.